Dense cellular network deployments relying on the use of Massive multiple-in multiple-out (MIMO) technology are becoming very attractive candidates for future radio access technologies. This is partly due to the promise of Massive MIMO for providing very large throughput increases per base station (BS), due to its ability to multiplex a large number of high-rate streams over each transmission resource element. Massive MIMO is very attractive when it is used over dense (small cell) deployments, and it can then translate to massive throughput increases per unit area with respect to existing deployments.
Non-uniform traffic-load distribution is considered to be a major challenge in small cell networks. If the load cannot be balanced efficiently, the performance gains that are expected as a result of the increased density of network access points (due to use of small cells) may be distributed in a very non-uniform manner within the user population. Various load-balancing techniques have been proposed for dynamically arranging user load across small cells. These techniques are generally designed considering traditional physical (PHY) layer approaches, where one BS serves at most one user at a certain frequency and time resource. But it is well accepted by now that major gains in the PHY layer are expected due to multi-view (MU)-MIMO and especially Massive MIMO.
Current technologies for load balancing in Massive MIMO have a number of important limitations. First, given that the user rates in a MU-MIMO, transmission are not simply a function of large-scale signal-to-interference plus noise ratio (SINR), but in general depend on the scheduling set and the channel realization, the resulting load-balancing techniques are not extendable in any straightforward resource-efficient manner. Furthermore, the nature of reciprocity-based Massive MIMO TDD makes large scale SINR in a link between a user and all BSs in proximity available given a single uplink pilot broadcast from the user. In this context, a centralized processor can determine the UT-BS associations of the user population among a set of BSs that would serve these users, without involving exchanges with the users. Using such a central controller to both perform load balancing (i.e., to balance the user load across BSs) among the BSs and to schedule transmissions at each of the BSs places computational burden to the central controller.